Today's modern business enterprises require and make use of sophisticated information systems to acquire vital insights into the performance or prospective future performance of their business relative to goals, market metrics, competitive information, and the like. This class of information products is known in the field today as Management Information Systems (MIS) or Business Intelligence (BI) tools. In addition businesses seek better ways to identify the right strategies and new ideas that can help them grow, and information solutions supporting these objectives are often referred to as Collaboration Technologies, and Innovation Management Systems. Collectively these information systems fall under the general category of Enterprise and Marketing Intelligence Systems and represent a critical part of today's business software and information systems marketplace.
While data management and reporting technologies have advanced to become adept at efficiently retrieving information stored in these systems and generating reports, the problem that plagues all these systems is the lack of a unifying information framework, or ontology, that provides a stable and fundamental frame of reference that is absolute and consistently meaningful across all domains for gleaning business insights or for facilitating value creation. The lack of an ontology means that evaluations on the information gathered are highly subjective and dependent on interpretation, and that each information domain tends to exist as an island where local rules prevail, rather than as a part of an integrated whole. The problems this creates for business are innumerable; consequently MIS and BI systems today, while enabling better informed decisions, have failed to deliver on their promise of transforming management decision making. For example, these systems can easily track the sales results and underlying demographics for a particular market, but utterly fail at providing any empirically defensible prediction, save extrapolation of past results, around whether these results are sustainable or what impact a new idea will have. More generally, the lack of a valid unifying and quantifiable frame of reference for business insight and intelligence means that compromises are made in making decisions and projections into future business impacts are largely guesswork. This problem has always existed in business information analysis and decision making, and it is a root cause of many mistaken beliefs and failures in business information technology initiatives.
An example of market modeling created by Anthony Ulwick and called Outcome Driven Innovation (ODI) creates an empirically valid estimator of market demand by holistically identifying the Jobs that customers and key participants in the consumption chain are trying to get done in a particular market and then collecting quantitative data on Importance and satisfaction levels associated with all of these jobs and with the desired outcomes associated with a specific core job of interest. This data is then analyzed to identify needs that are underserved (representing opportunities for new products and services) and those that are over-served (representing areas that are ready for being disrupted). A proprietary index called the opportunity score is used to determine the strength of the underlying market conditions driving these findings, and this score has been shown to be a valid empirical estimator of customer demand/sentiment and hence the consequential business value of fulfilling the market needs appropriately. The practice of researching and analyzing markets in this fashion is what is referred to as the ODI methodology.
The ODI methodology possesses four critical attributes that collectively make it uniquely valuable for business analysis. First, the use of the Jobs framework facilitates the description of an interaction a customer or key influencer may have with current or yet-to-be designed products and services and the measurement of these in a meaningful unit of analysis. This is important to obtaining insights and making informed decisions on questions where the objects of interest are parts of interconnected systems like in virtually all business matters. Today's MIS, BI, and Innovation Management systems lack this unifying framework and so do little to facilitate meaningful comparisons and analyses within and across the inherently disparate information domains of the system (e.g. competitive information, customer market information, product management information, R&D, etc.). Second, the measurement system used by the methodology provides direct quantitative measurement of the fundamental driver of business outcomes—customer demand, and this measurement system is both reproducible and repeatable. Third, the actual measurements taken are internally consistent; that is they report on the same dimensions of importance and satisfaction irrespective of whether jobs or outcomes are being studied and whether the job of interest is a functional job, an emotional job, or a related job. This therefore means that the methodology enables disparate variables of successful business endeavors, such as emotional factors, functional factors, and performance factors, to be compared directly to one another for prioritization without transformation. And fourth, the numerical data collected are normalized by an indexing method to have the same market meaning regardless of the factor being studied and are scaled in a manner that directly reflects the significance of the metric in market terms. This ensures that comparisons across factors are not just qualitatively valid but also quantitatively correct and easily extrapolated to real business impacts. For these reasons the foundation of ODI presents an information platform for business analysis that is fundamentally superior to all constructs that have preceded it.